On September 1st, 2024, Ultralytics announced the release of “YOLOv11: Advancing Object Detection and Segmentation Models.” YOLOv11 builds upon YOLOv8’s versatile architecture, optimizing it further for object detection, segmentation, and pose estimation tasks across edge and cloud devices.
YOLOv11 comes in five versions-v11-n, v11-s, v11-m, v11-l, and v11-x-each offering different parameter counts and complexities to cater to various deployment requirements, from real-time applications on limited hardware to high-performance needs. This model suite includes improvements to mAP (Mean Average Precision) and speed for both bounding box and segmentation masks, providing enhanced accuracy and efficiency in diverse environments.
Key innovations in YOLOv11 include the Enhanced Attention Mechanism (EAM), which dynamically prioritizes essential image features, and optimized architecture layers for faster inference. These features make YOLOv11 exceptionally adaptable for tasks requiring precise segmentation, like medical imaging, autonomous navigation, and urban planning.
Stay tuned to discover more about YOLOv11’s capabilities and how it can transform your computer vision projects.
- YOLOv11 GitHub: github.com/ultralytics/yolov11)
- Notebook: www.kaggle.com/code/deepakat002/yolov11-object-detection
- Data for object detection tasks: www.kaggle.com/datasets/deepakat002/roundabout-aerial-images-yolo-data
- How to annotate data for YOLO models: th-cam.com/video/v-HIYfOqQeU/w-d-xo.html&ab_channel=coderzero
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